dbt Artifacts Package

This package builds a mart of tables and views describing the project it is installed in. In pre V1 versions of the package, the artifacts dbt produces were uploaded to the warehouse, hence the name of the package. That's no longer the case, but the name has stuck!

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for more information.

Supported Data Warehouses

The package currently supports

• Databricks ✅
• Spark ✅
• Snowflake ✅
• Google BigQuery ✅

Models included:

dim_dbt__current_models
dim_dbt__exposures
dim_dbt__models
dim_dbt__seeds
dim_dbt__snapshots
dim_dbt__sources
dim_dbt__tests
fct_dbt__invocations
fct_dbt__model_executions
fct_dbt__seed_executions
fct_dbt__snapshot_executions
fct_dbt__test_executions

See the generated dbt docs site for documentation on each model.

Quickstart

1. Add this package to your packages.yml:

packages:
  - package: brooklyn-data/dbt_artifacts
    version: 2.4.2

👷 Make sure to fix at least the minor version, to avoid issues when a new release is open. See the notes on upgrading below for more detail.

2. Run dbt deps to install the package

3. Add an on-run-end hook to your dbt_project.yml

   `on-run-end:
     - "{{ dbt_artifacts.upload_results(results) }}"`

   We recommend adding a conditional here so that the upload only occurs in your production environment, such as:

   on-run-end:
     - "{% if target.name == 'prod' %}{{ dbt_artifacts.upload_results(results) }}{% endif %}"`)

4. Run the tables!

   dbt run --select dbt_artifacts
   

Notes on upgrading

Due to the structure of the project, when additional fields are added, the package needs to be re-run to ensure the tables include the new field, or it will simply error on the hook. These changes will always be implemented within a new minor version, so make sure that the version you use in packages.yml reflects this.

To upgrade and re-build, update the version number within packages.yml and then run:

dbt deps
dbt run --select dbt_artifacts

Make sure this is updated in any database that you use your code base in.

Configuration

The following configuration can be used to specify where the raw (sources) data is uploaded, and where the dbt models are created:

models:
  ...
  dbt_artifacts:
    +database: your_destination_database # optional, default is your target database
    +schema: your_destination_schema # optional, default is your target schema
    staging:
      +database: your_destination_database # optional, default is your target database
      +schema: your_destination_schema # optional, default is your target schema
    sources:
      +database: your_sources_database # optional, default is your target database
      +schema: your sources_database # optional, default is your target schema

Note that model materializations and on_schema_change configs are defined in this package's dbt_project.yml, so do not set them globally in your dbt_project.yml (see docs on configuring packages):

Configurations made in your dbt_project.yml file will override any configurations in a package (either in the dbt_project.yml file of the package, or in config blocks).

Environment Variables

If the project is running in dbt Cloud, the following five columns (https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-environment-variables#special-environment-variables) will be automatically populated in the fct_dbt__invocations model:

• dbt_cloud_project_id
• dbt_cloud_job_id
• dbt_cloud_run_id
• dbt_cloud_run_reason_category
• dbt_cloud_run_reason

To capture other environment variables in the fct_dbt__invocations model in the env_vars column, add them to the env_vars variable in your dbt_project.yml. Note that environment variables with secrets (DBT_ENV_SECRET_) can't be logged.

vars:
  env_vars: [
    'ENV_VAR_1',
    'ENV_VAR_2',
    '...'
  ]

dbt Variables

To capture dbt variables in the fct_dbt__invocations model in the dbt_vars column, add them to the dbt_vars variable in your dbt_project.yml.

vars:
  dbt_vars: [
    'var_1',
    'var_2',
    '...'
  ]

Upgrading from 1.x to >=2.0.0

If you were using the following variables:

vars:
  dbt_artifacts_database: your_db
  dbt_artifacts_schema: your_schema

You must now move these to the following model configs:

models:
  ...
  dbt_artifacts:
    sources:
      +database: your_db
      +schema: your_schema

That's because the raw tables are now managed as dbt models. Be aware of any impact that generate_database_name and generate_schema_name macros may have on the final database/schema.

Migrating From <1.0.0 to >=1.0.0

To migrate your existing data from the dbt-artifacts versions <=0.8.0, a helper macro and guide is provided. This migration uses the old fct_* and dim_* models' data to populate the new sources. The steps to use the macro are as follows:

1. If not already completed, run dbt run-operation create_dbt_artifacts_tables to make your source tables.
2. Run dbt run-operation migrate_from_v0_to_v1 --args '<see-below-for-arguments>'.
3. Verify that the migration completes successfully.
4. Manually delete any database objects (sources, staging models, tables/views) from the previous dbt-artifacts version.

The arguments for migrate_from_v0_to_v1 are as follows:

argument	description
old_database	the database of the <1.0.0 output (fct_/dim_) models
old_schema	the schema of the <1.0.0 output (fct_/dim_) models
new_database	the target database that the artifact sources are in
new_schema	the target schema that the artifact sources are in

The old and new database/schemas do not have to be different, but it is explicitly defined for flexible support.

An example operation is as follows:

dbt run-operation migrate_from_v0_to_v1 --args '{old_database: analytics, old_schema: dbt_artifacts, new_database: analytics, new_schema: artifact_sources}'

Creating custom marts tables

Multiple modelled dim and fct models have been provided for ease of use, but we recognise that some use cases may require custom ones. To this end, you can disable all but the raw sources tables using the following in your dbt_project.yml file:

# dbt_project.yml

models:
  dbt_artifacts:
    +enabled: false
    sources:
      +enabled: true

In these sources tables, you will find a JSON column all_results which contains a JSON blob of the results object used, which you can use in your own analysis:

• exposures
• models
• seeds
• snapshots
• sources
• tests

Acknowledgements

Thank you to Tails.com for initial development and maintenance of this package. On 2021/12/20, the repository was transferred from the Tails.com GitHub organization to Brooklyn Data Co.

The macros in the early versions package were adapted from code shared by Kevin Chan and Jonathan Talmi of Snaptravel.

Thank you for sharing your work with the community!